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            <h1><a href="https://nn.labml.ai/transformers/mlm/index.html">Masked Language Model (MLM)</a></h1>
<p>This is a <a href="https://pytorch.org">PyTorch</a> implementation of Masked Language Model (MLM)  used to pre-train the BERT model introduced in the paper <a href="https://arxiv.org/abs/1810.04805">BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding</a>.</p>
<h2>BERT Pretraining</h2>
<p>BERT model is a transformer model. The paper pre-trains the model using MLM and with next sentence prediction. We have only implemented MLM here.</p>
<h3>Next sentence prediction</h3>
<p>In <em>next sentence prediction</em>, the model is given two sentences <code  class="highlight"><span></span><span class="n">A</span></code>
 and <code  class="highlight"><span></span><span class="n">B</span></code>
 and the model makes a binary prediction whether <code  class="highlight"><span></span><span class="n">B</span></code>
 is the sentence that follows <code  class="highlight"><span></span><span class="n">A</span></code>
 in the actual text. The model is fed with actual sentence pairs 50% of the time and random pairs 50% of the time. This classification is done while applying MLM. <em>We haven&#x27;t implemented this here.</em></p>
<h2>Masked LM</h2>
<p>This masks a percentage of tokens at random and trains the model to predict the masked tokens. They <strong>mask 15% of the tokens</strong> by replacing them with a special <code  class="highlight"><span></span><span class="p">[</span><span class="n">MASK</span><span class="p">]</span></code>
 token.</p>
<p>The loss is computed on predicting the masked tokens only. This causes a problem during fine-tuning and actual usage since there are no <code  class="highlight"><span></span><span class="p">[</span><span class="n">MASK</span><span class="p">]</span></code>
 tokens  at that time. Therefore we might not get any meaningful representations.</p>
<p>To overcome this <strong>10% of the masked tokens are replaced with the original token</strong>, and another <strong>10% of the masked tokens are replaced with a random token</strong>. This trains the model to give representations about the actual token whether or not the input token at that position is a <code  class="highlight"><span></span><span class="p">[</span><span class="n">MASK</span><span class="p">]</span></code>
. And replacing with a random token causes it to give a representation that has information from the context as well; because it has to use the context to fix randomly replaced tokens.</p>
<h2>Training</h2>
<p>MLMs are harder to train than autoregressive models because they have a smaller training signal. i.e. only a small percentage of predictions are trained per sample.</p>
<p>Another problem is since the model is bidirectional, any token can see any other token. This makes the &quot;credit assignment&quot; harder. Let&#x27;s say you have the character level model trying to predict <code  class="highlight"><span></span><span class="n">home</span> <span class="o">*</span><span class="n">s</span> <span class="n">where</span> <span class="n">i</span> <span class="n">want</span> <span class="n">to</span> <span class="n">be</span></code>
. At least during the early stages of the training, it&#x27;ll be super hard to figure out why the replacement for <code  class="highlight"><span></span><span class="o">*</span></code>
 should be <code  class="highlight"><span></span><span class="n">i</span></code>
, it could be anything from the whole sentence. Whilst, in an autoregressive setting the model will only have to use <code  class="highlight"><span></span><span class="n">h</span></code>
 to predict <code  class="highlight"><span></span><span class="n">o</span></code>
 and <code  class="highlight"><span></span><span class="n">hom</span></code>
 to predict <code  class="highlight"><span></span><span class="n">e</span></code>
 and so on. So the model will initially start predicting with a shorter context first and then learn to use longer contexts later. Since MLMs have this problem it&#x27;s a lot faster to train if you start with a smaller sequence length initially and then use a longer sequence length later.</p>
<p>Here is <a href="https://nn.labml.ai/transformers/mlm/experiment.html">the training code</a> for a simple MLM model. </p>

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